Battery Management and AI Technology Electronic Patent Technology Trend Survey Report
You can view patent information categorized by the following technical classifications: - Deterioration and Anomaly Estimation We have covered patent information related to technologies that estimate battery deterioration, lifespan (SOH), and anomalies based on learned and analyzed data such as collected voltage, current, and temperature. - Charge Level Estimation We have covered patent information related to technologies that estimate the state of charge (SOC) of batteries using machine learning. - Parameter Estimation We have covered patent information related to technologies that estimate future parameters of batteries, such as voltage, current, impedance, internal resistance, and temperature, using machine learning. - Failure Cause Analysis We have covered patent information related to technologies that estimate the causes of battery failures using machine learning when a battery fails. - Battery Management System We have covered patent information related to battery management systems (BMS) that utilize machine learning to determine target charge levels based on usage conditions, predict discharge profiles, and manage operations that maximize rewards.
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basic information
WEB Version With the introduction of renewable energy and the spread of electric vehicles, the importance of energy storage functions is increasing. This dynamic map investigates patent information related to energy storage functions utilizing machine learning and classifies technologies addressing various challenges. ■ Target Technologies Among AI (artificial intelligence) technologies, we focused on techniques that utilize machine learning to estimate parameters such as battery voltage, current, internal resistance, temperature, characteristics like charge amount, and conditions such as degradation, as well as techniques to infer causes when a battery fails. It also includes technologies used for managing batteries in power supply systems utilizing renewable energy. ■ Target Patent Information In this dynamic map, we consider the period during which the researched technologies do not become obsolete to be approximately five years. We investigated 531 domestic patents that were filed between July 1, 2016, and May 27, 2021, and issued between January 1, 2018, and May 27, 2021, organizing them into eight technology classifications.
Price information
Dynamic Map: [Price] 180,000 yen + tax Patent Guidebook: [Price] 80,000 yen + tax University Patent: [Price] 30,000 yen + tax All companies addressing the target market: [Price] 30,000 yen + tax NEO Report: [Price] 180,000 yen + tax Web preview is available. We will introduce the actual products. Please feel free to consult with us.
Price range
P3
Delivery Time
P2
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Data Resources, Inc. since its establishment in 1995, has been working as an information sales agent, selling market research reports and annual subscription services, and also providing custom research and consulting services to Japanese and overseas customers. Data Resources offers such services to marketing and research personnel at telecommunication operators, IT services providers, semiconductor makers, automobile manufacturers, auto component manufacturers, electronics manufacturers, and materials manufacturers. Data Resources provides customers with the highest quality solutions available.
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